Apply \(\Delta \Delta C_T\) analysis to each target gene in the input data frame. Target and reference genes must be provided as paired efficiency (E) and Ct columns located after the experimental design columns. columns.
Usage
ANOVA_DDCt(
x,
numOfFactors,
numberOfrefGenes,
mainFactor.column,
block,
analysisType = "anova",
mainFactor.level.order = NULL,
p.adj = "none",
plot = FALSE,
plotType = "RE",
analyseAllTarget = TRUE
)Arguments
- x
A data frame containing experimental design columns, target gene E/Ct column pairs, and reference gene E/Ct column pairs. Reference gene columns must be located at the end of the data frame.
- numOfFactors
Integer. Number of experimental factor columns (excluding
repand optionalblock).- numberOfrefGenes
Integer. Number of reference genes. Each reference gene must be represented by two columns (E and Ct).
- mainFactor.column
Column index or name of the factor for which relative expression is calculated. When
analysisType = "ancova", remaining factors are treated as covariates.- block
Character or
NULL. Name of the blocking factor column. When a qPCR experiment is done in multiple qPCR plates, variation resulting from the plates may interfere with the actual amount of gene expression. One solution is to conduct each plate as a randomized block so that at least one replicate of each treatment and control is present on a plate. Block effect is usually considered as random and its interaction with any main effect is not considered.- analysisType
Character string specifying the analysis type; one of
"anova"(default) or"ancova".- mainFactor.level.order
Optional character vector specifying the order of levels for the main factor. If
NULL, the first observed level is used as the calibrator. If provided, the first element of the vector is used as the calibrator level.- p.adj
Method for p-value adjustment. See
p.adjust.- plot
Logical; if
FALSE, per gene-plots are not generated.- plotType
Plot scale to use:
"RE"for relative expression or"log2FC"for log2 fold change.- analyseAllTarget
Logical or character. If
TRUE(default), all target genes are analysed. Alternatively, a character vector specifying the names (names of their Efficiency columns) of target genes to be analysed.
Value
An object containing expression table, lm models, residuals, raw data and ANOVA table for each gene.
- \(\Delta \Delta C_T\) combined expression table
object$combinedFoldChange- ANOVA table
object$perGene$gene_name$ANOVA_table- lm ANOVA
object$perGene$gene_name$lm_ANOVA- lm ANCOVA
object$perGene$gene_name$lm_ANCOVA- Residuals
resid(object$perGene$gene_name$lm_ANOVA)- log2FC_Plot
object$perGene$gene_name$log2FC_Plot- RE_Plot
object$perGene$gene_name$RE_Plot
Details
\(\Delta \Delta C_T\) analysis is performed for
the mainFactor.column based on a full model factorial
experiment by default. However, if ancova, the analysisType argument,
analysis of covariance is performed for the levels of the mainFactor.column and the other factors are
treated as covariates. if the interaction between the main factor and the covariate is significant, ANCOVA is not appropriate.
ANCOVA is basically used when a factor is affected by uncontrolled quantitative covariate(s).
For example, suppose that wDCt of a target gene in a plant is affected by temperature. The gene may
also be affected by drought. Since we already know that temperature affects the target gene, we are
interested to know if the gene expression is also altered by the drought levels. We can design an
experiment to understand the gene behavior at both temperature and drought levels at the same time.
The drought is another factor (the covariate) that may affect the expression of our gene under the
levels of the first factor i.e. temperature. The data of such an experiment can be analyzed by ANCOVA
or using ANOVA based on a factorial experiment. ANCOVA is done
even there is only one factor (without covariate or factor variable).
Examples
data1 <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))
ANOVA_DDCt(x = data1,
numOfFactors = 2,
numberOfrefGenes = 2,
block = "block",
mainFactor.column = 2,
plot = FALSE,
p.adj = "none")
#> NOTE: Results may be misleading due to involvement in interactions
#> ANOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> block 1 1.836 1.836 1.2206 0.2866
#> factor2 2 1.725 0.862 0.5732 0.5756
#> factor1 1 58.636 58.636 38.9781 1.574e-05 ***
#> factor2:factor1 2 0.566 0.283 0.1882 0.8304
#> Residuals 15 22.565 1.504
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ANCOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> block 1 1.836 1.836 1.3495 0.2614
#> factor1 1 58.636 58.636 43.0936 4.816e-06 ***
#> factor2 2 1.725 0.862 0.6338 0.5427
#> Residuals 17 23.131 1.361
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Expression table
#> contrast RE log2FC pvalue sig LCL UCL se Lower.se.RE
#> 1 L1 1.0000 0.0000 1.0000 0.000 0.0000 0.8318 0.5618
#> 2 L2 vs L1 0.6525 -0.6159 0.3672 0.198 2.1504 0.6218 0.4240
#> 3 L3 vs L1 0.6818 -0.5525 0.3818 0.226 2.0568 0.7245 0.4126
#> Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 1.7799 0.0000 0.0000
#> 2 1.0041 -0.9478 -0.4003
#> 3 1.1266 -0.9130 -0.3344
#> *** The L1 level was used as calibrator.
#> NOTE: Results may be misleading due to involvement in interactions
#> ANOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> block 1 2.2118 2.2118 2.1255 0.163088
#> factor2 2 13.6670 6.8335 6.5669 0.007707 **
#> factor1 1 30.9361 30.9361 29.7291 4.301e-05 ***
#> factor2:factor1 2 3.1292 1.5646 1.5035 0.250470
#> Residuals 17 17.6902 1.0406
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ANCOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> block 1 2.2118 2.2118 2.0185 0.171592
#> factor1 1 30.9361 30.9361 28.2326 3.967e-05 ***
#> factor2 2 13.6670 6.8335 6.2363 0.008275 **
#> Residuals 19 20.8194 1.0958
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Expression table
#> contrast RE log2FC pvalue sig LCL UCL se Lower.se.RE
#> 1 L1 1.0000 0.0000 1.0000 0.0000 0.0000 0.5922 0.6633
#> 2 L2 vs L1 0.5053 -0.9847 0.0704 . 0.2040 1.2516 0.5838 0.3372
#> 3 L3 vs L1 0.2780 -1.8471 0.0021 ** 0.1122 0.6884 0.5217 0.1936
#> Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 1.5076 0.0000 0.0000
#> 2 0.7573 -1.4759 -0.6570
#> 3 0.3991 -2.6518 -1.2866
#> *** The L1 level was used as calibrator.
#>
#> Relative Expression
#> gene contrast RE log2FC pvalue sig LCL UCL se Lower.se.RE
#> 1 E.PO L1 1.0000 0.0000 1.0000 0.0000 0.0000 0.8318 0.5618
#> 2 E.PO L2 vs L1 0.6525 -0.6159 0.3672 0.1980 2.1504 0.6218 0.4240
#> 3 E.PO L3 vs L1 0.6818 -0.5525 0.3818 0.2260 2.0568 0.7245 0.4126
#> 4 E.PAO5 L1 1.0000 0.0000 1.0000 0.0000 0.0000 0.5922 0.6633
#> 5 E.PAO5 L2 vs L1 0.5053 -0.9847 0.0704 . 0.2040 1.2516 0.5838 0.3372
#> 6 E.PAO5 L3 vs L1 0.2780 -1.8471 0.0021 ** 0.1122 0.6884 0.5217 0.1936
#> Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 1.7799 0.0000 0.0000
#> 2 1.0041 -0.9478 -0.4003
#> 3 1.1266 -0.9130 -0.3344
#> 4 1.5076 0.0000 0.0000
#> 5 0.7573 -1.4759 -0.6570
#> 6 0.3991 -2.6518 -1.2866
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref.csv", package = "rtpcr"))
ANOVA_DDCt(
x = data2,
numOfFactors = 1,
numberOfrefGenes = 1,
block = NULL,
mainFactor.column = 1,
plot = FALSE,
p.adj = "none")
#> ANOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Condition 1 2.1361 2.13607 7.8465 0.04875 *
#> Residuals 4 1.0889 0.27223
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ANCOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Condition 1 2.1361 2.13607 7.8465 0.04875 *
#> Residuals 4 1.0889 0.27223
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Expression table
#> contrast RE log2FC pvalue sig LCL UCL se
#> 1 control 1.0000 0.0000 1.0000 0.0000 0.0000 0.0601
#> 2 treatment vs control 0.4373 -1.1933 0.0488 * 0.1926 0.9927 0.4218
#> Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 0.9592 1.0425 0.0000 0.0000
#> 2 0.3264 0.5858 -1.5985 -0.8908
#> *** The control level was used as calibrator.
#> ANOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Condition 1 6.0401 6.0401 61.907 0.00141 **
#> Residuals 4 0.3903 0.0976
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ANCOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Condition 1 6.0401 6.0401 61.907 0.00141 **
#> Residuals 4 0.3903 0.0976
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Expression table
#> contrast RE log2FC pvalue sig LCL UCL se
#> 1 control 1.0000 0.0000 1.0000 0.0000 0.0000 0.1302
#> 2 treatment vs control 4.0185 2.0067 0.0014 ** 2.4598 6.5649 0.2193
#> Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 0.9137 1.0944 0.0000 0.0000
#> 2 3.4518 4.6783 1.7237 2.3361
#> *** The control level was used as calibrator.
#> ANOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Condition 1 0.7776 0.7776 6.5731 0.06239 .
#> Residuals 4 0.4732 0.1183
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ANCOVA table
#> Analysis of Variance Table
#>
#> Response: wDCt
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Condition 1 0.7776 0.7776 6.5731 0.06239 .
#> Residuals 4 0.4732 0.1183
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Expression table
#> contrast RE log2FC pvalue sig LCL UCL se
#> 1 control 1.0000 0.00 1.0000 0.0000 0.0000 0.1850
#> 2 treatment vs control 1.6472 0.72 0.0624 . 0.9595 2.8279 0.2113
#> Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 0.8796 1.1368 0.0000 0.0000
#> 2 1.4228 1.9069 0.6219 0.8335
#> *** The control level was used as calibrator.
#>
#> Relative Expression
#> gene contrast RE log2FC pvalue sig LCL UCL se
#> 1 C2H2_26 control 1.0000 0.0000 1.0000 0.0000 0.0000 0.0601
#> 2 C2H2_26 treatment vs control 0.4373 -1.1933 0.0488 * 0.1926 0.9927 0.4218
#> 3 C2H2_01 control 1.0000 0.0000 1.0000 0.0000 0.0000 0.1302
#> 4 C2H2_01 treatment vs control 4.0185 2.0067 0.0014 ** 2.4598 6.5649 0.2193
#> 5 C2H2_12 control 1.0000 0.0000 1.0000 0.0000 0.0000 0.1850
#> 6 C2H2_12 treatment vs control 1.6472 0.7200 0.0624 . 0.9595 2.8279 0.2113
#> Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC
#> 1 0.9592 1.0425 0.0000 0.0000
#> 2 0.3264 0.5858 -1.5985 -0.8908
#> 3 0.9137 1.0944 0.0000 0.0000
#> 4 3.4518 4.6783 1.7237 2.3361
#> 5 0.8796 1.1368 0.0000 0.0000
#> 6 1.4228 1.9069 0.6219 0.8335